Finding related tables from data lakes [1]}, [2]}, [3]}, has a wide spectrum of real application scenarios.
There are two sub-tasks of finding related tables, namely table union search and joinable table search.
In this paper, we mainly focus on the problem of table union search, which has been recognized as a crucial task in dataset discovery from data lakes [4]}, [2]}, [6]}, [7]}, [8]}, [9]}, [3]}.
Given a query table and a collection of data lake tables, table union search aims to find all tables that are unionable with the query table.
To determine whether two tables are unionable, existing solutions first identify all pairs of unionable columns from the two tables based on the column representation, such as bag of tokens or bag of word embeddings.
They then devise some mechanism to aggregate the column-level results to compute the table unionability score.
